CN112328961A - On-line monitoring device quality evaluation system based on fault tree and Bayesian network - Google Patents
On-line monitoring device quality evaluation system based on fault tree and Bayesian network Download PDFInfo
- Publication number
- CN112328961A CN112328961A CN202011215813.4A CN202011215813A CN112328961A CN 112328961 A CN112328961 A CN 112328961A CN 202011215813 A CN202011215813 A CN 202011215813A CN 112328961 A CN112328961 A CN 112328961A
- Authority
- CN
- China
- Prior art keywords
- data
- index
- omega
- monitoring device
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012806 monitoring device Methods 0.000 title claims abstract description 38
- 238000013441 quality evaluation Methods 0.000 title claims abstract description 15
- 238000011156 evaluation Methods 0.000 claims abstract description 35
- 230000002159 abnormal effect Effects 0.000 claims abstract description 23
- 238000012544 monitoring process Methods 0.000 claims abstract description 19
- 230000009191 jumping Effects 0.000 claims abstract description 7
- 238000001514 detection method Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 30
- 230000005856 abnormality Effects 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 10
- 230000007423 decrease Effects 0.000 claims description 9
- 238000000034 method Methods 0.000 claims description 8
- 230000007547 defect Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 4
- 230000005672 electromagnetic field Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 235000021354 omega 7 monounsaturated fatty acids Nutrition 0.000 claims description 3
- 235000021315 omega 9 monounsaturated fatty acids Nutrition 0.000 claims description 3
- 230000035945 sensitivity Effects 0.000 claims description 3
- 238000001303 quality assessment method Methods 0.000 claims 1
- 238000003745 diagnosis Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Mathematical Physics (AREA)
- Strategic Management (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Algebra (AREA)
- Software Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Quality & Reliability (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Game Theory and Decision Science (AREA)
- Probability & Statistics with Applications (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Primary Health Care (AREA)
Abstract
The invention discloses a quality evaluation system of an online monitoring device based on a fault tree and a Bayesian network, which comprises the following steps: selecting a sliding window according to the acquisition frequency and characteristics of the monitored quantity, and intercepting a data set of the forward time T from the current moment from the online monitoring data stream; detecting data by repeating, missing, jumping and the like, if an abnormal mode is found, carrying out the next step, otherwise, returning to the step; according to the online monitoring device quality evaluation system based on the fault tree and the Bayesian network, the field monitoring device does not need to be disassembled and inspected, and the normal operation of the power grid is not influenced; an online monitoring device state evaluation system is established, the device state is automatically evaluated, the investment of detection personnel is reduced, and the workload of first-line workers is reduced; the requirement on the experience level of workers is not high; the system analyzes the data acquired by the monitoring device, the data demand source is single, and the device diagnosis mode is simple and efficient.
Description
Technical Field
The invention belongs to the field of quality evaluation and monitoring of online monitoring devices, and particularly relates to an online monitoring device quality evaluation system based on a fault tree and a Bayesian network.
Background
Through the development of the last decade, the on-line monitoring technology of the equipment is widely applied to the monitoring of the power equipment of the power grid, and plays an important role in ensuring the safety and stable operation of the power grid. The monitoring device can cover all or more than 220 kV and part of important 110 kV transformer substations and overhead transmission lines, can monitor key parameters of a main equipment body of the power transmission and transformation and key parameters of an external environment in real time, and supports technical personnel and management personnel of each level of operation and inspection to master the key parameters and states of the equipment; through monitoring unusual defective devices in real time, the operation and inspection personnel can be guided to timely overhaul the devices, the safe and stable operation of the power grid devices is guaranteed, the power failure times of the devices are reduced, the reliability of the devices is improved, and the device fault tripping is effectively reduced.
However, the online monitoring device is affected by external factors such as a complex strong electromagnetic environment of high-voltage equipment, a severe operating condition, lightning and the like for a long time in the operating process, and problems such as sensor abnormality, communication interruption, power failure, software defects and the like easily occur, so that data distortion and a high false alarm rate frequently occur, the captured equipment state abnormality cannot be captured in time, and the device itself is abnormal but alarms frequently. Under the condition, the light person causes false alarm and function failure of the device, and the field operation and maintenance difficulty is increased; the serious person causes misoperation of equipment protection and even causes safety production accidents. The main reasons for the problems are two aspects, on one hand, the product quality of part of manufacturers is inherently insufficient, so that various software and hardware faults are frequent; on the other hand, the device is lack of necessary operation and maintenance management means for monitoring the device, and the failure of the device is frequently caused due to the insufficient inspection and maintenance.
Therefore, the effectiveness and the reliability of the monitoring device are evaluated, the quality of online monitoring data is improved, and on one hand, operation and maintenance personnel can be helped to maintain and overhaul the online monitoring device in a targeted manner; on the other hand, the fault judgment opportunity of the power grid body can be effectively avoided from being delayed, and the running reliability of the power grid equipment is improved.
Therefore, an online monitoring device quality evaluation system based on a fault tree and a Bayesian network is provided.
Disclosure of Invention
The invention mainly aims to provide an online monitoring device quality evaluation system based on a fault tree and a Bayesian network, which can effectively solve the problems in the background art.
In order to achieve the purpose, the invention adopts the technical scheme that: the online monitoring device quality evaluation system based on the fault tree and the Bayesian network comprises the following steps:
step 1: selecting a sliding window according to the acquisition frequency and characteristics of the monitored quantity, and intercepting a data set of the forward time T from the current moment from the online monitoring data stream;
step 2: detecting data by repeating, missing, jumping and the like, if an abnormal mode exists, performing the next step, otherwise, returning to the step 1;
and step 3: judging a data flow abnormal mode and a situation that multiple kinds of abnormalities coexist possibly according to the device data abnormal model;
and 4, step 4: calculating a device fault Bayesian network by using the device fault statistical data as prior probability;
and 5: correcting a Bayesian network calculation result by using the data abnormal mode as supplementary information;
step 6: respectively calculating the ratio of the data volume of each abnormal mode to the normal data volume in the sliding window as a fuzzy index score;
and 7: establishing a fuzzy evaluation matrix according to the fuzzy index score in the step 6 and the fault probability of each component in the step 5;
and 8: and performing matrix multiplication operation on the weight set and the fuzzy evaluation matrix, and calculating the operation reliability evaluation result of the online monitoring device.
The construction of a preferred bayesian network comprises the steps of:
s1: establishing a device operation reliability evaluation index system, and defining an index set S ═ { S1, S2, S3, … …, S17} and a comment set V ═ V1, V2, V3}, wherein V1-V3 respectively represent normal, abnormal and fault states of the device;
s2: the index weight is calculated by taking into account the correlation between the index weight and the failure probability (P) and the degree of importance (I) of each index, and the item takes the index risk (R) as the basis for weight calculation, where R is P × I. The fault probability of each index can be obtained according to the calculation method in the previous section, the importance degree of each index can be calculated by using an analytic hierarchy process, and an index comparison matrix A is constructed by pairwise comparison of indexes A1-A9; secondly, calculating the characteristic vector and the relative weight of the matrix A; finally, consistency check is carried out on the relative weights, the consistency of results of the weights in pairwise comparison is guaranteed, the calculation results of the index weights are multiplied by the fault probability, and the results are normalized to obtain an index risk weight set omega which is { omega 1, omega 2, omega 3, omega 4, omega 5, omega 6, omega 7, omega 8, omega 9, omega 10, omega 11 and omega 12 };
s3: determining a membership function, wherein the item selects intermediate trapezoidal distribution, and the expression is as follows:
s4: establishing a fuzzy evaluation matrix, substituting all index scores into the formula to obtain the fuzzy evaluation matrix, namely
In the formula: rij represents the membership degree of the ith index to the jth comment; n represents the number of indexes; m represents the number of comments;
s5: calculating the operation quality evaluation result of the device, and performing matrix multiplication operation on the weight set omega and the fuzzy evaluation matrix R to obtain a model evaluation result, namely
And finally, according to the maximum membership criterion, the comment corresponding to the maximum value in the evaluation result B is the evaluation result.
Preferably, the A1-A9 indexes are pairwise compared to construct an index comparison matrix A, wherein A1: data interruption (instantaneous), wherein the abnormality is data loss, the duration is generally in the order of minutes, and the main reasons are software defects and poor communication quality;
a2: data interruption (long-term), compared with instantaneous interruption, the duration of the abnormality is generally of a daily level and mainly caused by communication interruption, battery electric energy exhaustion, software defects or parameter errors and the like;
a3: data repetition, wherein if the abnormality is caused by communication interruption, the data is generally a negative value or a limit value; if the sensor completely fails, the value is a random value in a certain measuring range. Such anomalies are typically not zero;
a4: data fixed offset, namely a fixed deviation exists between a detection value and an actual measurement value, and is caused by the fixed offset of the sensor;
a5: data is zero, and when the sensor sensitivity is reduced, a small change in the monitored quantity is difficult to capture, so the data remains zero;
a6: the data continuously increases/decreases, the data continuously increases/decreases along with time, and if the trend keeps the same direction for a long time, the trend is due to the drift of the sensor; otherwise, the device state is changed;
a7: data jumping, the equipment state changes in a step manner to cause the monitoring value to increase or decrease suddenly, and the monitoring value has certain continuity;
a8: data jitter, under severe operating conditions such as switch on/off and full load, the strong electromagnetic field which changes violently can cause data to fluctuate up and down with the mean value as the center;
a9: outliers, also known as discrete values, refer to values where the data deviates far from the statistical mean, and external environmental interference is a major source of such anomalies.
Preferably, the fault tree-based monitoring method includes the following steps:
step one, setting a time sliding window for real-time data acquired by an online monitoring device, and taking the data in the sliding window as a characteristic data set for evaluating the monitoring device;
step two, taking the distribution situation of abnormal values, the distribution situation of continuous identical values, the null value situation and the change situation of the variation coefficient in the feature data set as criteria, and applying the criteria to the feature data set;
acquiring corresponding discrimination values, and giving corresponding weight to each discrimination value according to the condition of the emphasis on the criterion;
and step four, adding and summing the discrimination values with the weights to obtain the state value of the monitoring device.
Compared with the prior art, the invention has the following beneficial effects: according to the online monitoring device quality evaluation system based on the fault tree and the Bayesian network, the field monitoring device does not need to be disassembled and inspected, and the normal operation of the power grid is not influenced; an online monitoring device state evaluation system is established, the device state is automatically evaluated, the investment of detection personnel is reduced, and the workload of first-line workers is reduced; the requirement on the experience level of workers is not high; the system analyzes the data acquired by the monitoring device, the data demand source is single, and the device diagnosis mode is simple and efficient.
Drawings
FIG. 1 is a model diagram of an evaluation index system for an online monitoring device constructed by FTA according to the present invention;
FIG. 2 is an evaluation index system model diagram of an online monitoring device constructed based on FTA.
FIG. 3 is a diagram of a Bayesian network constructed in accordance with the present invention.
Fig. 4 is a diagram illustrating the reliability evaluation procedure of the on-line monitoring device for power transmission and transformation equipment according to the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
As shown in fig. 1-4, the technical scheme adopted by the invention is as follows: the quality evaluation system of the online monitoring device based on the fault tree and the Bayesian network comprises the following steps:
step 1: selecting a sliding window according to the acquisition frequency and characteristics of the monitored quantity, and intercepting a data set of the forward time T from the current moment from the online monitoring data stream;
step 2: detecting data by repeating, missing, jumping and the like, if an abnormal mode exists, performing the next step, otherwise, returning to the step 1;
and step 3: judging a data flow abnormal mode and a situation that multiple kinds of abnormalities coexist possibly according to the device data abnormal model;
and 4, step 4: calculating a device fault Bayesian network by using the device fault statistical data as prior probability;
and 5: correcting a Bayesian network calculation result by using the data abnormal mode as supplementary information;
step 6: respectively calculating the ratio of the data volume of each abnormal mode to the normal data volume in the sliding window as a fuzzy index score;
and 7: establishing a fuzzy evaluation matrix according to the fuzzy index score in the step 6 and the fault probability of each component in the step 5;
and 8: and performing matrix multiplication operation on the weight set and the fuzzy evaluation matrix, and calculating the operation reliability evaluation result of the online monitoring device.
The construction of a preferred bayesian network comprises the steps of:
s1: establishing a device operation reliability evaluation index system, and defining an index set S ═ { S1, S2, S3, … …, S17} and a comment set V ═ V1, V2, V3}, wherein V1-V3 respectively represent normal, abnormal and fault states of the device;
s2: the index weight is calculated by taking into account the correlation between the index weight and the failure probability (P) and the degree of importance (I) of each index, and the item takes the index risk (R) as the basis for weight calculation, where R is P × I. The fault probability of each index can be obtained according to the calculation method in the previous section, the importance degree of each index can be calculated by using an analytic hierarchy process, and an index comparison matrix A is constructed by pairwise comparison of indexes A1-A9; secondly, calculating the characteristic vector and the relative weight of the matrix A; finally, consistency check is carried out on the relative weights, the consistency of results of the weights in pairwise comparison is guaranteed, the calculation results of the index weights are multiplied by the fault probability, and the results are normalized to obtain an index risk weight set omega which is { omega 1, omega 2, omega 3, omega 4, omega 5, omega 6, omega 7, omega 8, omega 9, omega 10, omega 11 and omega 12 };
s3: determining a membership function, wherein the item selects intermediate trapezoidal distribution, and the expression is as follows:
s4: establishing a fuzzy evaluation matrix, substituting all index scores into the formula to obtain the fuzzy evaluation matrix, namely
In the formula: rij represents the membership degree of the ith index to the jth comment; n represents the number of indexes; m represents the number of comments;
s5: calculating the operation quality evaluation result of the device, and performing matrix multiplication operation on the weight set omega and the fuzzy evaluation matrix R to obtain a model evaluation result, namely
And finally, according to the maximum membership criterion, the comment corresponding to the maximum value in the evaluation result B is the evaluation result.
Preferably, the A1-A9 indexes are pairwise compared to construct an index comparison matrix A, wherein A1: data interruption (instantaneous), wherein the abnormality is data loss, the duration is generally in the order of minutes, and the main reasons are software defects and poor communication quality;
a2: data interruption (long-term), compared with instantaneous interruption, the duration of the abnormality is generally of a daily level and mainly caused by communication interruption, battery electric energy exhaustion, software defects or parameter errors and the like;
a3: data repetition, wherein if the abnormality is caused by communication interruption, the data is generally a negative value or a limit value; if the sensor completely fails, the value is a random value in a certain measuring range. Such anomalies are typically not zero;
a4: data fixed offset, namely a fixed deviation exists between a detection value and an actual measurement value, and is caused by the fixed offset of the sensor;
a5: data is zero, and when the sensor sensitivity is reduced, a small change in the monitored quantity is difficult to capture, so the data remains zero;
a6: the data continuously increases/decreases, the data continuously increases/decreases along with time, and if the trend keeps the same direction for a long time, the trend is due to the drift of the sensor; otherwise, the device state is changed;
a7: data jumping, the equipment state changes in a step manner to cause the monitoring value to increase or decrease suddenly, and the monitoring value has certain continuity;
a8: data jitter, under severe operating conditions such as switch on/off and full load, the strong electromagnetic field which changes violently can cause data to fluctuate up and down with the mean value as the center;
a9: outliers, also known as discrete values, refer to values where the data deviates far from the statistical mean, and external environmental interference is a major source of such anomalies.
Preferably, the fault tree-based monitoring method includes the following steps:
step one, setting a time sliding window for real-time data acquired by an online monitoring device, and taking the data in the sliding window as a characteristic data set for evaluating the monitoring device;
step two, taking the distribution situation of abnormal values, the distribution situation of continuous identical values, the null value situation and the change situation of the variation coefficient in the feature data set as criteria, and applying the criteria to the feature data set;
acquiring corresponding discrimination values, and giving corresponding weight to each discrimination value according to the condition of the emphasis on the criterion;
and step four, adding and summing the discrimination values with the weights to obtain the state value of the monitoring device.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. The online monitoring device quality evaluation system based on the fault tree and the Bayesian network is characterized by comprising the following steps:
step 1: selecting a sliding window according to the acquisition frequency and characteristics of the monitored quantity, and intercepting a data set of the forward time T from the current moment from the online monitoring data stream;
step 2: detecting data by repeating, missing, jumping and the like, if an abnormal mode exists, performing the next step, otherwise, returning to the step 1;
and step 3: judging a data flow abnormal mode and a situation that multiple kinds of abnormalities coexist possibly according to the device data abnormal model;
and 4, step 4: calculating a device fault Bayesian network by using the device fault statistical data as prior probability;
and 5: correcting a Bayesian network calculation result by using the data abnormal mode as supplementary information;
step 6: respectively calculating the ratio of the data volume of each abnormal mode to the normal data volume in the sliding window as a fuzzy index score;
and 7: establishing a fuzzy evaluation matrix according to the fuzzy index score in the step 6 and the fault probability of each component in the step 5;
and 8: and performing matrix multiplication operation on the weight set and the fuzzy evaluation matrix, and calculating the operation reliability evaluation result of the online monitoring device.
2. The online monitoring device quality assessment system based on the fault tree and the Bayesian network as claimed in claim 1, wherein the Bayesian network is constructed by the following steps:
s1: establishing a device operation reliability evaluation index system, and defining an index set S ═ { S1, S2, S3, … …, S17} and a comment set V ═ V1, V2, V3}, wherein V1-V3 respectively represent normal, abnormal and fault states of the device;
s2: the index weight is calculated by taking into account the correlation between the index weight and the failure probability (P) and the degree of importance (I) of each index, and the item takes the index risk (R) as the basis for weight calculation, where R is P × I. The fault probability of each index can be obtained according to the calculation method in the previous section, the importance degree of each index can be calculated by using an analytic hierarchy process, and an index comparison matrix A is constructed by pairwise comparison of indexes A1-A9; secondly, calculating the characteristic vector and the relative weight of the matrix A; finally, consistency check is carried out on the relative weights, the consistency of results of the weights in pairwise comparison is guaranteed, the calculation results of the index weights are multiplied by the fault probability, and the results are normalized to obtain an index risk weight set omega which is { omega 1, omega 2, omega 3, omega 4, omega 5, omega 6, omega 7, omega 8, omega 9, omega 10, omega 11 and omega 12 };
s3: determining a membership function, wherein the item selects intermediate trapezoidal distribution, and the expression is as follows:
s4: establishing a fuzzy evaluation matrix, substituting all index scores into the formula to obtain the fuzzy evaluation matrix, namely
In the formula: rij represents the membership degree of the ith index to the jth comment; n represents the number of indexes; m represents the number of comments;
s5: calculating the operation quality evaluation result of the device, and performing matrix multiplication operation on the weight set omega and the fuzzy evaluation matrix R to obtain a model evaluation result, namely
And finally, according to the maximum membership criterion, the comment corresponding to the maximum value in the evaluation result B is the evaluation result.
3. The bayesian network according to claim 2, characterized by: and the A1-A9 indexes are pairwise compared to construct an index comparison matrix A, wherein A1: data interruption (instantaneous), wherein the abnormality is data loss, the duration is generally in the order of minutes, and the main reasons are software defects and poor communication quality;
a2: data interruption (long-term), compared with instantaneous interruption, the duration of the abnormality is generally of a daily level and mainly caused by communication interruption, battery electric energy exhaustion, software defects or parameter errors and the like;
a3: data repetition, wherein if the abnormality is caused by communication interruption, the data is generally a negative value or a limit value; if the sensor completely fails, the value is a random value in a certain measuring range. Such anomalies are typically not zero;
a4: data fixed offset, namely a fixed deviation exists between a detection value and an actual measurement value, and is caused by the fixed offset of the sensor;
a5: data is zero, and when the sensor sensitivity is reduced, a small change in the monitored quantity is difficult to capture, so the data remains zero;
a6: the data continuously increases/decreases, the data continuously increases/decreases along with time, and if the trend keeps the same direction for a long time, the trend is due to the drift of the sensor; otherwise, the device state is changed;
a7: data jumping, the equipment state changes in a step manner to cause the monitoring value to increase or decrease suddenly, and the monitoring value has certain continuity;
a8: data jitter, under severe operating conditions such as switch on/off and full load, the strong electromagnetic field which changes violently can cause data to fluctuate up and down with the mean value as the center;
a9: outliers, also known as discrete values, refer to values where the data deviates far from the statistical mean, and external environmental interference is a major source of such anomalies.
4. The fault tree of claim 1, wherein: the monitoring method based on the fault tree comprises the following steps:
step one, setting a time sliding window for real-time data acquired by an online monitoring device, and taking the data in the sliding window as a characteristic data set for evaluating the monitoring device;
step two, taking the distribution situation of abnormal values, the distribution situation of continuous identical values, the null value situation and the change situation of the variation coefficient in the feature data set as criteria, and applying the criteria to the feature data set;
acquiring corresponding discrimination values, and giving corresponding weight to each discrimination value according to the condition of the emphasis on the criterion;
and step four, adding and summing the discrimination values with the weights to obtain the state value of the monitoring device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011215813.4A CN112328961A (en) | 2020-11-04 | 2020-11-04 | On-line monitoring device quality evaluation system based on fault tree and Bayesian network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011215813.4A CN112328961A (en) | 2020-11-04 | 2020-11-04 | On-line monitoring device quality evaluation system based on fault tree and Bayesian network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112328961A true CN112328961A (en) | 2021-02-05 |
Family
ID=74323711
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011215813.4A Pending CN112328961A (en) | 2020-11-04 | 2020-11-04 | On-line monitoring device quality evaluation system based on fault tree and Bayesian network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112328961A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113176986A (en) * | 2021-04-28 | 2021-07-27 | 一汽解放汽车有限公司 | Internet of vehicles data quality determination method and device, computer equipment and storage medium |
| CN113468473A (en) * | 2021-06-30 | 2021-10-01 | 清华大学 | Real-time evaluation method and system for running state of outdoor fixed large-scale mechanical equipment |
| CN115222306A (en) * | 2022-09-21 | 2022-10-21 | 中国地质环境监测院(自然资源部地质灾害技术指导中心) | Data quality evaluation method and system for geological disaster monitoring |
| CN116304957A (en) * | 2023-05-17 | 2023-06-23 | 成都交大光芒科技股份有限公司 | On-line identification method for monitoring state mutation of power supply and transformation equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102496069A (en) * | 2011-12-07 | 2012-06-13 | 山东电力集团公司青岛供电公司 | Cable multimode safe operation evaluation method based on fuzzy analytic hierarchy process (FAHP) |
| CN109284901A (en) * | 2018-08-24 | 2019-01-29 | 国网山东省电力公司电力科学研究院 | A kind of metal supervision evaluation method of Transmission Tower-line |
| CN109685340A (en) * | 2018-12-11 | 2019-04-26 | 国网山东省电力公司青岛供电公司 | A kind of controller switching equipment health state evaluation method and system |
| CN110059963A (en) * | 2019-04-20 | 2019-07-26 | 北京交通大学 | A kind of tunnel risk evaluating method based on fuzzy polymorphism Bayesian network |
| CN111414692A (en) * | 2020-03-19 | 2020-07-14 | 南京市计量监督检测院 | Reliability evaluation method for pressure gauge calibration console based on Bayesian correction model |
-
2020
- 2020-11-04 CN CN202011215813.4A patent/CN112328961A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102496069A (en) * | 2011-12-07 | 2012-06-13 | 山东电力集团公司青岛供电公司 | Cable multimode safe operation evaluation method based on fuzzy analytic hierarchy process (FAHP) |
| CN109284901A (en) * | 2018-08-24 | 2019-01-29 | 国网山东省电力公司电力科学研究院 | A kind of metal supervision evaluation method of Transmission Tower-line |
| CN109685340A (en) * | 2018-12-11 | 2019-04-26 | 国网山东省电力公司青岛供电公司 | A kind of controller switching equipment health state evaluation method and system |
| CN110059963A (en) * | 2019-04-20 | 2019-07-26 | 北京交通大学 | A kind of tunnel risk evaluating method based on fuzzy polymorphism Bayesian network |
| CN111414692A (en) * | 2020-03-19 | 2020-07-14 | 南京市计量监督检测院 | Reliability evaluation method for pressure gauge calibration console based on Bayesian correction model |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113176986A (en) * | 2021-04-28 | 2021-07-27 | 一汽解放汽车有限公司 | Internet of vehicles data quality determination method and device, computer equipment and storage medium |
| CN113468473A (en) * | 2021-06-30 | 2021-10-01 | 清华大学 | Real-time evaluation method and system for running state of outdoor fixed large-scale mechanical equipment |
| CN113468473B (en) * | 2021-06-30 | 2022-10-18 | 清华大学 | Real-time evaluation method and system for running state of outdoor fixed large-scale mechanical equipment |
| CN115222306A (en) * | 2022-09-21 | 2022-10-21 | 中国地质环境监测院(自然资源部地质灾害技术指导中心) | Data quality evaluation method and system for geological disaster monitoring |
| CN116304957A (en) * | 2023-05-17 | 2023-06-23 | 成都交大光芒科技股份有限公司 | On-line identification method for monitoring state mutation of power supply and transformation equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112328961A (en) | On-line monitoring device quality evaluation system based on fault tree and Bayesian network | |
| CN106655522B (en) | A kind of main station system suitable for electric grid secondary equipment operation management | |
| CN103647276B (en) | A kind of quality of power supply early warning system and method thereof | |
| CN111145517B (en) | Artificial intelligence electric line fire risk real-time early warning method and system | |
| CN110557457A (en) | Intelligent electricity safety early warning and cloud protection system | |
| CN109274097A (en) | A kind of electric power system transient stability method for situation assessment based on Random Matrices Theory | |
| CN105301453B (en) | A kind of partial discharge monitoring method for early warning | |
| CN117040138A (en) | Power distribution cabinet operation dynamic safety evaluation system | |
| CN108919044B (en) | Active identification method for unit distribution power grid faults based on mutual verification mechanism | |
| CN103729804A (en) | On-line decision support method for responding to power quality early warning | |
| CN117977582B (en) | Real-time monitoring method for transformer substation load based on digital twin technology | |
| CN117332215B (en) | High-low voltage power distribution cabinet abnormal fault information remote monitoring system | |
| CN111144549A (en) | Microgrid attack identification method based on convolutional neural network and microgrid coordination controller | |
| CN115096366A (en) | SF6 ring main unit running state monitoring system and state evaluation method thereof | |
| CN117811213B (en) | Low-voltage power distribution cabinet fault diagnosis system based on data analysis | |
| CN106325258B (en) | Relay protection device state evaluation method based on online monitoring information | |
| CN117609861A (en) | BP neural network algorithm and temperature measurement system capable of being used for temperature prediction of electrical equipment of transformer substation | |
| CN112182960A (en) | Power transformer state risk assessment method based on Bayesian network | |
| Li et al. | Real time evaluation algorithm for measurement performance of substation voltage transformer based on artificial neural network | |
| CN116797403A (en) | Communication station power supply and distribution safety early warning method | |
| CN111158338A (en) | Chemical risk monitoring method based on principal component analysis | |
| CN117791869B (en) | Data online monitoring method and system based on intelligent power distribution cabinet | |
| CN117269843B (en) | On-line monitoring method and system for neutral line running state of secondary current loop | |
| Song et al. | Research on multi-parameter identification technology of secondary equipment monitoring and evaluation in smart substation | |
| CN114047372B (en) | Voltage characteristic-based platform region topology identification system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211217 Address after: 211100 room 1204, 12 / F, building a, Mingjia science and technology building, 99 Shengli Road, Jiangning District, Nanjing, Jiangsu Province (Jiangning Development Zone) Applicant after: Jiangsu prospect Credit Suisse Technology Development Co.,Ltd. Address before: Room 1801, 18 / F, block a, Mingjia science and technology building, 99 Shengli Road, Jiangning District, Nanjing, Jiangsu 210000 (Jiangning Development Zone) Applicant before: Jiangsu haituorunda Technology Development Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221227 Address after: Room 1801, Floor 18, Block A, Mingjia Science and Technology Building, No. 99, Shengli Road, Jiangning District, Nanjing City, Jiangsu Province, 211100 (Jiangning Development Zone) Applicant after: Jiangsu haituorunda Technology Development Co.,Ltd. Address before: 211100 room 1204, 12 / F, building a, Mingjia science and technology building, 99 Shengli Road, Jiangning District, Nanjing, Jiangsu Province (Jiangning Development Zone) Applicant before: Jiangsu prospect Credit Suisse Technology Development Co.,Ltd. |
|
| TA01 | Transfer of patent application right |